Vanishing islands in the sky? A comparison of correlation- and mechanism-based forecasts of range dynamics for montane salamanders under climate change
Data files
Dec 09, 2019 version files 1.13 GB
-
Absences.csv
-
GroundTemperatureJordani.R
-
jord_015.asc
-
jord_046.asc
-
jord_074.asc
-
jord_105.asc
-
jord_135.asc
-
jord_166.asc
-
jord_196.asc
-
jord_227.asc
-
jord_258.asc
-
jord_288.asc
-
jord_319.asc
-
jord_349.asc
-
jord_dem.asc
-
jord_header.txt
-
jord_str.asc
-
jord_tci.asc
-
jord_tot.asc
-
Localities.csv
-
maxcoef_out.csv
-
metcalfi_015.asc
-
metcalfi_046.asc
-
metcalfi_074.asc
-
metcalfi_105.asc
-
metcalfi_135.asc
-
metcalfi_166.asc
-
metcalfi_196.asc
-
metcalfi_227.asc
-
metcalfi_258.asc
-
metcalfi_288.asc
-
metcalfi_319.asc
-
metcalfi_349.asc
-
metcalfi_dem.asc
-
Metcalfi_header.txt
-
metcalfi_str.asc
-
metcalfi_tci.asc
-
metcalfi_tot.asc
-
mincoef_out.csv
-
mont_015.asc
-
mont_046.asc
-
mont_074.asc
-
mont_105.asc
-
mont_135.asc
-
mont_166.asc
-
mont_196.asc
-
mont_227.asc
-
mont_258.asc
-
mont_288.asc
-
mont_319.asc
-
mont_349.asc
-
mont_dem.asc
-
mont_header.txt
-
mont_str.asc
-
mont_tci.asc
-
mont_tot.asc
Abstract
Forecasting the effects of climate change on species and populations is a fundamental goal of conservation biology, especially for montane endemics which seemingly are under the greatest threat of extinction given their association with cool, high elevation habitats. Species distribution models (also known as niche models) predict where on the landscape there is suitable habitat for a species of interest. Correlative niche modeling, the most commonly employed approach to predict species’ distributions, relies on correlations between species’ localities and current environmental data. This type of model could spuriously forecast less future suitable habitat because species’ current distributions may not adequately represent their thermal tolerance, and future climate conditions may not be analogous to current conditions. We compared the predicted distributions for three montane species of Plethodon salamanders in the southern Appalachian Mountains of North America using a correlative modeling approach and a mechanistic model. The mechanistic model incorporates species-specific physiology, morphology, and behavior to predict an annual energy budget on the landscape. Both modeling approaches performed well at predicting the species’ current distributions and predicted that all species could persist in habitats at higher elevation through 2085. The mechanistic model predicted more future suitable habitat than the correlative model. We attribute these differences to the mechanistic approach being able to model shifts in key range-limiting biological processes (changes in surface activity time and energy costs) that the correlative approach cannot. Choice of global circulation model (GCM) contributed significantly to distribution predictions, with a tenfold difference in future suitability based on GCM, indicating that GCM variability should be either directly included in models of species distributions or, indirectly, through the use of multi-model ensemble averages. Our results indicate that correlative models are over-predicting habitat loss for montane species, suggesting a critical need to incorporate mechanisms into forecasts of species’ range dynamics.
Methods
Localities of Plethodontid salamanders in the southern Applachian Mountains are from the US National Museum of Natural History Herpetology Collection. Ground temperature input rasters for radiative heating and topographic convergence were developed using GRASS and the provided digital elevation model. Stream distance calculated from the USGS stream vectors datasets for each state.
Usage notes
Localities for presence and absence occurance points for three montane species, Plethodon jordani, Plethodon metcalfi, and Plethodon montanus. Absences are locations where a plethdontid salamander, but not the species of interest is found.
Ascii files necessary for running ground temperature model for the southern Appalachians developed by Fridley 2009 for the Smoky Mountains. Each species has ascii files for elevation (dem), average monthly radiative heating (numbered by julian date), total yearly radiative heating, stream distance, and topographic convergence index. Ascii headers were removed for running the code but available in a header.txt file. Example R code for computing ground temperature for Plethodon jordani.